class_docstr: Wrap a NeuronSimulator and calibrate it to map from the predicted activation space to the
  actual neuron activation space.
docstr: Apply the learned calibration to a sequence of values.
docstr_tok: ["Apply","the","learned","calibration","to","a","sequence","of","values","."]
code: def apply_calibration(self, values: Sequence[float]) -> list[float]:
      """Apply the learned calibration to a sequence of values."""
  
imports: from __future__ import annotations
  import asyncio
  from abc import abstractmethod
  from typing import Optional, Sequence
  import numpy
  from neuron_explainer.activations.activations import ActivationRecord
  from neuron_explainer.explanations.explanations import ActivationScale
  from neuron_explainer.explanations.simulator import NeuronSimulator, SequenceSimulation
  from sklearn import linear_model
label_desc: Use image node_id 4 for calling the CalibratedNeuronSimulator obj's underlying member method code with example usage: obj.apply_calibration(values) without return types